code: def __init__(
      self,
      base_path,
      train_file=None,
      dev_file=None,
      test_file=None,
      predict_file=None,
      label_file=None,
      label_list=None,
      train_file_with_header=False,
      dev_file_with_header=False,
      test_file_with_header=False,
      predict_file_with_header=False,
      tokenizer=None,
      max_seq_len=128,
      split_char="\002",
      start_token="<s>",
      end_token="</s>",
      unk_token="<unk>",
  ):
      self.split_char = split_char
      self.start_token = start_token
      self.end_token = end_token
      self.unk_token = unk_token
      super(GenerationDataset, self).__init__(
          base_path=base_path,
          train_file=train_file,
          dev_file=dev_file,
          test_file=test_file,
          predict_file=predict_file,
          label_file=label_file,
          label_list=label_list,
          train_file_with_header=train_file_with_header,
          dev_file_with_header=dev_file_with_header,
          test_file_with_header=test_file_with_header,
          predict_file_with_header=predict_file_with_header,
          tokenizer=tokenizer,
          max_seq_len=max_seq_len,
      )
  
imports: import io
  import csv
  import collections
  import numpy
  from tqdm import tqdm
  from paddlehub.compat.datasets.base_dataset import InputExample, BaseDataset
  from paddlehub.utils.log import logger
  from paddlehub.text.tokenizer import CustomTokenizer
  from paddlehub.text.bert_tokenizer import BertTokenizer
label_desc: Use image node_id 1 to create a new GenerationDataset object from inherited base classes: BaseNLPDataset with example: obj = GenerationDataset(base_path, train_file, dev_file, test_file, predict_file, label_file, label_list, train_file_with_header, dev_file_with_header, test_file_with_header, predict_file_with_header, tokenizer, max_seq_len, split_char, start_token, end_token, unk_token)